factorScorePca: Factor Analysis by Principal Component Analysis (PCA)

Description

Perform principal component factor analysis on a covariance matrix or data matrix.

Usage

factorScorePca(x, factors = 2, covmat = NULL, cor = FALSE, 
rotation = c("varimax", "none"), 
scoresMethod = c("none", "regression", "Bartlett"))

Value

An object of class "factorScorePca" with components:

call: The matched call.
loadings: A matrix of loadings, one column for each factor. This is of class "loadings" if rotation = "varimax": see loadings for its print method; It is a plain matrix if rotation = "none".
communality: The common variance.
uniquenesses: The uniquenesses/specific variance computed.
covariance: The robust/classical covariance matrix.
correlation: The robust/classical correlation matrix.
usedMatrix: The used matrix (running matrix). It may be the covariance or correlation matrix according to the value of cor.
reducedCorrelation: NULL. The reduced correlation matrix, reducedCorrelation is calculated in factorScorePfa.R.
factors: The argument factors.
method: The method: always "pca".
scores: If requested, a matrix of scores. NULL if x is missing.
scoringCoef: The scoring coefficients. NULL if x is missing.
meanF: The sample mean of the scores. NULL if x is missing.
corF: The sample correlation matrix of the scores. NULL if x is missing.
scoresMethod: The argument scoresMethod.
n.obs: The number of observations if available. NULL if x is missing.
center: The center of the data.
eigenvalues: The eigenvalues of the usedMatrix.

Arguments

x: A numeric matrix or an object that can be coerced to a numeric matrix.
factors: The number of factors to be fitted.
covmat: A covariance matrix, or a covariance list as returned by cov.wt. Of course, correlation matrices are covariance matrices.
cor: A logical value indicating whether the calculation should use the covariance matrix (cor = FALSE) or the correlation matrix (cor = TRUE).
rotation: character. "none" or "varimax": it will be called with first argument the loadings matrix, and should return a list with component loadings giving the rotated loadings, or just the rotated loadings.
scoresMethod: Type of scores to produce, if any. The default is "none", "regression" gives Thompson's scores, "Bartlett" gives Bartlett's weighted least-squares scores.

Author

Ying-Ying Zhang (Robert) robertzhangyying@qq.com

Details

Other feasible usages are:

factorScorePca(factors, covmat)

factorScorePca(x, factors, rotation, scoresMethod)

If x is missing, then the following components of the result will be NULL: scores, ScoringCoef, meanF, corF, and n.obs.

References

Zhang, Y. Y. (2013), An Object Oriented Solution for Robust Factor Analysis.

Examples

Run this code


data(stock611)
R611=cor(stock611[,3:12]); R611

## covmat is a matrix
fsPca1=factorScorePca(factors = 3, covmat = R611); fsPca1

## covmat is a list
covx <- rrcov::Cov(stock611[,3:12])
covmat <- list(cov=rrcov::getCov(covx), center=rrcov::getCenter(covx), n.obs=covx@n.obs)
fsPca2=factorScorePca(factors = 3, covmat = covmat); fsPca2

## fsPca3 contains scores etc.
fsPca3=factorScorePca(x = stock611[,3:12], factors = 2, cor = TRUE, rotation = "varimax", 
scoresMethod = "regression"); fsPca3

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